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Format Key Files using LLVM Style (#403) 

* format using llvm style

* merge and format
This commit is contained in:
Tian Jin 2019-12-19 13:27:15 -05:00 committed by Tian Jin
parent 06a968d4a1
commit a6a40cf989
6 changed files with 277 additions and 251 deletions
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22
src/builder/frontend_dialect_transformer.cpp
View File

@ -382,7 +382,7 @@ private:
auto end = default_str.find(",", start + 1);
if (end == std::string::npos) {
end = default_str.find("}", start + 1);
if (end != std::string::npos && end > start+1) {
if (end != std::string::npos && end > start + 1) {
r.push_back(std::stoi(default_str.substr(start + 1, end)));
}
break;
@ -401,7 +401,7 @@ private:
auto end = default_str.find(",", start + 1);
if (end == std::string::npos) {
end = default_str.find("}", start + 1);
if (end != std::string::npos && end > start+1) {
if (end != std::string::npos && end > start + 1) {
r.push_back(std::stof(default_str.substr(start + 1, end)));
}
break;
@ -420,7 +420,7 @@ private:
auto end = default_str.find(",", start + 1);
if (end == std::string::npos) {
end = default_str.find("}", start + 1);
if (end != std::string::npos && end > start+1) {
if (end != std::string::npos && end > start + 1) {
r.push_back(default_str.substr(start + 1, end));
}
break;
@ -529,18 +529,19 @@ private:
}
std::vector<mlir::NamedAttribute> attributes;
//for (auto [attr_name, attr_type, attr_default] : attrs) {
for (auto oneAttr: attrs) {
// for (auto [attr_name, attr_type, attr_default] : attrs) {
for (auto oneAttr : attrs) {
std::string attr_name;
std::string attr_type;
std::string attr_default;
std::tie (attr_name, attr_type, attr_default) = oneAttr;
std::tie(attr_name, attr_type, attr_default) = oneAttr;
if (attr_type != "") {
auto attr = ImportNodeAttr(node, attr_name, attr_type, attr_default);
attributes.push_back(attr);
} else {
// TODO: the attributes need special handling
//std::cout << "missing " << node.op_type() << " " << attr_name << std::endl;
// std::cout << "missing " << node.op_type() << " " << attr_name <<
// std::endl;
}
}
@ -575,17 +576,18 @@ private:
}
std::vector<mlir::NamedAttribute> attributes;
for (auto oneAttr: attrs) {
for (auto oneAttr : attrs) {
std::string attr_name;
std::string attr_type;
std::string attr_default;
std::tie (attr_name, attr_type, attr_default) = oneAttr;
std::tie(attr_name, attr_type, attr_default) = oneAttr;
if (attr_type != "") {
auto attr = ImportNodeAttr(node, attr_name, attr_type, attr_default);
attributes.push_back(attr);
} else {
// TODO: the attributes need special handling
//std::cout << "missing " << node.op_type() << " " << attr_name << std::endl;
// std::cout << "missing " << node.op_type() << " " << attr_name <<
// std::endl;
}
}

132
src/compiler/dialect/krnl/krnl_ops.cpp
View File

@ -9,8 +9,6 @@
#include <iostream>
#include <queue>
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallBitVector.h"
#include "mlir/Dialect/AffineOps/AffineOps.h"
#include "mlir/Dialect/StandardOps/Ops.h"
#include "mlir/IR/Block.h"
@ -23,6 +21,8 @@
#include "mlir/IR/Operation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/DialectConversion.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallBitVector.h"
#include "src/compiler/dialect/krnl/krnl_helper.hpp"
@ -31,7 +31,7 @@
using namespace mlir;
namespace mlir {
KrnlOpsDialect::KrnlOpsDialect(MLIRContext* context)
KrnlOpsDialect::KrnlOpsDialect(MLIRContext *context)
: Dialect(getDialectNamespace(), context) {
addOperations<
#define GET_OP_LIST
@ -44,29 +44,30 @@ KrnlOpsDialect::KrnlOpsDialect(MLIRContext* context)
// KrnlDefineLoopsOp
//===----------------------------------------------------------------------===//
void KrnlDefineLoopsOp::build(
Builder* builder, OperationState& result, int64_t num_loops) {
void KrnlDefineLoopsOp::build(Builder *builder, OperationState &result,
int64_t num_loops) {
// Create the same number of dimension handlers as the number of
// dimensions in the associated integer set.
result.types.append(num_loops, LoopType::get(builder->getContext()));
result.addAttribute(
getNumLoopsAttrName(), builder->getI32IntegerAttr(num_loops));
result.addAttribute(getNumLoopsAttrName(),
builder->getI32IntegerAttr(num_loops));
}
void print(OpAsmPrinter& p, KrnlDefineLoopsOp& op) {
void print(OpAsmPrinter &p, KrnlDefineLoopsOp &op) {
auto numLoopAttr =
op.getAttrOfType<IntegerAttr>(KrnlDefineLoopsOp::getNumLoopsAttrName());
p << "krnl.define_loops " << numLoopAttr.getValue().getSExtValue();
}
ParseResult parseKrnlDefineLoopsOp(
OpAsmParser& parser, OperationState& result) {
ParseResult parseKrnlDefineLoopsOp(OpAsmParser &parser,
OperationState &result) {
// Parse the attribute indicating number of loops defined.
IntegerAttr numLoops;
auto& builder = parser.getBuilder();
auto &builder = parser.getBuilder();
auto intType = builder.getIntegerType(64);
if (parser.parseAttribute(numLoops, intType,
KrnlDefineLoopsOp::getNumLoopsAttrName(), result.attributes))
KrnlDefineLoopsOp::getNumLoopsAttrName(),
result.attributes))
return failure();
auto loopTypes = llvm::SmallVector<Type, 4>(
@ -79,29 +80,29 @@ ParseResult parseKrnlDefineLoopsOp(
// KrnlOptimizeLoopsOp
//===----------------------------------------------------------------------===//
void KrnlOptimizeLoopsOp::build(
Builder* builder, OperationState& result, int num_optimized_loops) {
result.types.append(
num_optimized_loops, LoopType::get(builder->getContext()));
void KrnlOptimizeLoopsOp::build(Builder *builder, OperationState &result,
int num_optimized_loops) {
result.types.append(num_optimized_loops,
LoopType::get(builder->getContext()));
// Create a region and a block for the body.
// Schedule intrinsics will be placed into this region.
Region* region = result.addRegion();
auto* body = new Block();
Region *region = result.addRegion();
auto *body = new Block();
region->push_back(body);
}
void print(OpAsmPrinter& p, KrnlOptimizeLoopsOp& op) {
void print(OpAsmPrinter &p, KrnlOptimizeLoopsOp &op) {
p << "krnl.optimize_loops ";
p.printRegion(op.region(), /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/true);
/*printBlockTerminators=*/true);
p << " : ";
p.printFunctionalType(op);
}
ParseResult parseKrnlOptimizeLoopsOp(
OpAsmParser& parser, OperationState& result) {
ParseResult parseKrnlOptimizeLoopsOp(OpAsmParser &parser,
OperationState &result) {
// Parse the schedule body region.
Region* region = result.addRegion();
Region *region = result.addRegion();
if (parser.parseRegion(*region, llvm::None, llvm::None))
return failure();
@ -142,21 +143,22 @@ ParseResult parseKrnlOptimizeLoopsOp(
* Then the bounds will be parsed as:
* %i0 = 10 to N : %i1 = M to 20
*/
void KrnlIterateOp::build(Builder* builder, OperationState& result,
KrnlIterateOperandPack operandPack) {
void KrnlIterateOp::build(Builder *builder, OperationState &result,
KrnlIterateOperandPack operandPack) {
// Record optimized loops and the number of such loops.
result.addOperands(operandPack.getOperands());
result.addAttribute(
KrnlIterateOp::getBoundsAttrName(), operandPack.getAttributes());
result.addAttribute(KrnlIterateOp::getBoundsAttrName(),
operandPack.getAttributes());
result.addAttribute(getNumOptimizedLoopsAttrName(),
result.addAttribute(
getNumOptimizedLoopsAttrName(),
builder->getI64IntegerAttr(operandPack.getNumOptimizedLoops()));
// Create a region and a block for the body. The arguments of the region are
// the loop induction variables; there can be multiple induction variables
// associated with the same krnl.iterate operation.
Region* bodyRegion = result.addRegion();
auto* body = new Block();
Region *bodyRegion = result.addRegion();
auto *body = new Block();
auto body_args = llvm::SmallVector<Type, 4>(
operandPack.getNumInputLoops(), IndexType::get(builder->getContext()));
body->addArguments(body_args);
@ -165,7 +167,7 @@ void KrnlIterateOp::build(Builder* builder, OperationState& result,
ensureTerminator(*bodyRegion, *builder, result.location);
}
void print(OpAsmPrinter& p, KrnlIterateOp& op) {
void print(OpAsmPrinter &p, KrnlIterateOp &op) {
p << "krnl.iterate(";
// Print optimized loops:
auto numOptimizedLoops = op.getNumOptimizedLoops();
@ -180,7 +182,7 @@ void print(OpAsmPrinter& p, KrnlIterateOp& op) {
auto operandItr = op.operand_begin() + numOptimizedLoops;
std::string delimiter;
for (auto& var : inductionVars) {
for (auto &var : inductionVars) {
p << delimiter;
p.printOperand(*operandItr++);
p << " -> ";
@ -194,25 +196,26 @@ void print(OpAsmPrinter& p, KrnlIterateOp& op) {
p << ")";
p.printRegion(op.bodyRegion(), /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/false);
/*printBlockTerminators=*/false);
}
ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
ParseResult parseKrnlIterateOp(OpAsmParser &parser, OperationState &result) {
auto builder = parser.getBuilder();
auto context = builder.getContext();
onnf::KrnlDialectOperandParser operandParser(parser);
// Parse optimized loops:
SmallVector<OpAsmParser::OperandType, 4> optimizedLoopRefs;
if (parser.parseOperandList(
optimizedLoopRefs, OpAsmParser::Delimiter::Paren) ||
if (parser.parseOperandList(optimizedLoopRefs,
OpAsmParser::Delimiter::Paren) ||
parser.resolveOperands(optimizedLoopRefs,
LoopType::get(result.getContext()), result.operands))
LoopType::get(result.getContext()),
result.operands))
return failure();
// Record how many optimized loops did we parse.
result.addAttribute(KrnlIterateOp::getNumOptimizedLoopsAttrName(),
builder.getI64IntegerAttr(optimizedLoopRefs.size()));
builder.getI64IntegerAttr(optimizedLoopRefs.size()));
// Parse input loops and their lower and upper bounds.
SmallVector<OpAsmParser::OperandType, 4> inductionVarRefs;
@ -222,16 +225,16 @@ ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
return failure();
// A function to parse a lower or upper bound.
auto parseBound = [&result, &builder, &parser, &operandParser, &boundMaps](
bool isUpper) -> ParseResult {
auto parseBound = [&result, &builder, &parser, &operandParser,
&boundMaps](bool isUpper) -> ParseResult {
// 'min' / 'max' prefixes are generally syntactic sugar, but are required if
// the map has multiple results.
bool failedToParsedMinMax =
failed(parser.parseOptionalKeyword(isUpper ? "min" : "max"));
// Try parse an SSA operand.
if (succeeded(operandParser.ParseOptionalOperand(
builder.getIndexType(), result.operands))) {
if (succeeded(operandParser.ParseOptionalOperand(builder.getIndexType(),
result.operands))) {
AffineMap map = builder.getSymbolIdentityMap();
boundMaps.emplace_back(AffineMapAttr::get(map));
return success();
@ -243,8 +246,8 @@ ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
llvm::SMLoc attrLoc = parser.getCurrentLocation();
Attribute boundAttr;
llvm::SmallVector<NamedAttribute, 1> tempBoundAttrContainer;
if (parser.parseAttribute(
boundAttr, builder.getIndexType(), "temp", tempBoundAttrContainer))
if (parser.parseAttribute(boundAttr, builder.getIndexType(), "temp",
tempBoundAttrContainer))
return failure();
if (auto affineMapAttr = boundAttr.dyn_cast<AffineMapAttr>()) {
@ -255,13 +258,15 @@ ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
auto map = affineMapAttr.getValue();
if (map.getNumDims() != numDims)
return parser.emitError(parser.getNameLoc(),
return parser.emitError(
parser.getNameLoc(),
"dim operand count and integer set dim count must match");
unsigned numDimAndSymbolOperands =
result.operands.size() - currentNumOperands;
if (numDims + map.getNumSymbols() != numDimAndSymbolOperands)
return parser.emitError(parser.getNameLoc(),
return parser.emitError(
parser.getNameLoc(),
"symbol operand count and integer set symbol count must match");
// If the map has multiple results, make sure that we parsed the min/max
@ -269,11 +274,11 @@ ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
if (map.getNumResults() > 1 && failedToParsedMinMax) {
if (isUpper)
return parser.emitError(attrLoc,
"upper loop bound affine map with multiple "
"results requires 'min' prefix");
"upper loop bound affine map with multiple "
"results requires 'min' prefix");
return parser.emitError(attrLoc,
"lower loop bound affine mapwith "
"multiple results requires 'max' prefix");
"lower loop bound affine mapwith "
"multiple results requires 'max' prefix");
}
boundMaps.emplace_back(AffineMapAttr::get(map));
return success();
@ -286,7 +291,7 @@ ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
}
};
bool keepParsing; // Do we keep parsing loops/bounds?
bool keepParsing; // Do we keep parsing loops/bounds?
do {
// Parse an input loop operand;
operandParser.ParseOperand(LoopType::get(context), result.operands);
@ -316,18 +321,18 @@ ParseResult parseKrnlIterateOp(OpAsmParser& parser, OperationState& result) {
// At this point, there shouldn't be any operands left to parse.
if (operandParser.hasOperandLeft())
return parser.emitError(parser.getCurrentLocation());
result.addAttribute(
KrnlIterateOp::getBoundsAttrName(), builder.getArrayAttr(boundMaps));
result.addAttribute(KrnlIterateOp::getBoundsAttrName(),
builder.getArrayAttr(boundMaps));
Region* region = result.addRegion();
SmallVector<Type, 4> inductionVarTypes(
inductionVarRefs.size(), builder.getIndexType());
Region *region = result.addRegion();
SmallVector<Type, 4> inductionVarTypes(inductionVarRefs.size(),
builder.getIndexType());
if (parser.parseRegion(*region, inductionVarRefs, inductionVarTypes))
return failure();
// Ensure iterate region is closed off with krnl.terminate.
KrnlIterateOp::ensureTerminator(
*region, parser.getBuilder(), result.location);
KrnlIterateOp::ensureTerminator(*region, parser.getBuilder(),
result.location);
return success();
}
@ -341,18 +346,19 @@ static LogicalResult verify(KrnlIterateOp op) {
// KrnlReturnLoopsOp
//===----------------------------------------------------------------------===//
void print(OpAsmPrinter& p, KrnlReturnLoopsOp& op) {
void print(OpAsmPrinter &p, KrnlReturnLoopsOp &op) {
p << "krnl.return_loops ";
p.printOperands(op.operand_begin(), op.operand_end());
}
ParseResult parseKrnlReturnLoopsOp(
OpAsmParser& parser, OperationState& result) {
ParseResult parseKrnlReturnLoopsOp(OpAsmParser &parser,
OperationState &result) {
// Parse the loops to return.
SmallVector<OpAsmParser::OperandType, 4> timestamp_dim_handlers;
if (parser.parseOperandList(timestamp_dim_handlers) ||
parser.resolveOperands(timestamp_dim_handlers,
LoopType::get(result.getContext()), result.operands))
LoopType::get(result.getContext()),
result.operands))
return failure();
return success();
@ -360,4 +366,4 @@ ParseResult parseKrnlReturnLoopsOp(
#define GET_OP_CLASSES
#include "src/compiler/krnl.cpp.inc"
} // namespace mlir
} // namespace mlir

16
src/compiler/dialect/krnl/krnl_ops.hpp
View File

@ -19,12 +19,12 @@
namespace mlir {
class KrnlOpsDialect : public Dialect {
public:
KrnlOpsDialect(MLIRContext* context);
public:
KrnlOpsDialect(MLIRContext *context);
static StringRef getDialectNamespace() { return "krnl"; }
/// Parse a type registered to this dialect.
Type parseType(DialectAsmParser& parser) const override {
Type parseType(DialectAsmParser &parser) const override {
if (succeeded(parser.parseOptionalKeyword("loop")))
return LoopType::get(parser.getBuilder().getContext());
@ -32,15 +32,15 @@ class KrnlOpsDialect : public Dialect {
}
/// Print a type registered to this dialect.
void printType(Type type, DialectAsmPrinter& os) const override {
void printType(Type type, DialectAsmPrinter &os) const override {
switch (type.getKind()) {
case KrnlTypes::Loop:
os << "loop";
return;
case KrnlTypes::Loop:
os << "loop";
return;
}
}
};
#define GET_OP_CLASSES
#include "src/compiler/krnl.hpp.inc"
} // namespace mlir
} // namespace mlir

297
src/compiler/pass/lower_frontend_to_krnl.cpp
View File

@ -11,17 +11,16 @@
#include <map>
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/Sequence.h"
#include "mlir/Dialect/AffineOps/AffineOps.h"
#include "mlir/Dialect/StandardOps/Ops.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/DialectConversion.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/Sequence.h"
#include "src/compiler/dialect/krnl/krnl_helper.hpp"
#include "src/compiler/dialect/krnl/krnl_ops.hpp"
#include "src/compiler/dialect/onnx/onnx_ops.hpp"
#include "src/compiler/pass/passes.hpp"
using namespace mlir;
@ -98,7 +97,7 @@ static Value *insertAllocAndDealloc(MemRefType type, Location loc,
// Make sure to allocate at the beginning of the block if
// all dimensions are known.
auto* parentBlock = alloc.getOperation()->getBlock();
auto *parentBlock = alloc.getOperation()->getBlock();
if (hasAllConstantDimensions(type))
alloc.getOperation()->moveBefore(&parentBlock->front());
@ -113,17 +112,17 @@ static Value *insertAllocAndDealloc(MemRefType type, Location loc,
// Determine if current function returns the result value of the
// current op being lowered. If it does then dealloc should not be
// inserted.
static bool checkInsertDealloc(Operation* currentOp) {
static bool checkInsertDealloc(Operation *currentOp) {
auto parentBlock = currentOp->getBlock();
bool insertDealloc = true;
parentBlock->walk([&insertDealloc, currentOp](ReturnOp op) {
assert(currentOp->getNumResults() < 2 &&
"No more than one result supported (for now).");
"No more than one result supported (for now).");
// If there is at least one result to investigate.
if (currentOp->getNumResults() > 0) {
auto result = currentOp->getResult(0);
for (const auto& operand : op.getOperands())
for (const auto &operand : op.getOperands())
if (operand == result)
insertDealloc = false;
}
@ -148,7 +147,7 @@ unsigned getMemRefEltSizeInBytes(MemRefType memRefType) {
// Get run-time dimension information for unknown dimensions used for
// broadcasting.
std::map<int, std::map<int, Value *> >
std::map<int, std::map<int, Value *>>
getBroadcastedDimInfo(Location loc, ConversionPatternRewriter &rewriter,
MemRefType memRefType, ArrayRef<Value *> operands) {
auto memRefShape = memRefType.getShape();
@ -196,15 +195,15 @@ getBroadcastedDimInfo(Location loc, ConversionPatternRewriter &rewriter,
// given operand.
std::vector<Value *>
getLoopIVsForBroadcasting(Location loc, ConversionPatternRewriter &rewriter,
ArrayRef<Value *> loopIVs, Value *operand,
std::map<int, Value *> broadcastedDims) {
ArrayRef<Value *> loopIVs, Value *operand,
std::map<int, Value *> broadcastedDims) {
// `operand` must has a ranked type. This should have been checked by the
// shape inference pass.
auto operandShape = operand->getType().cast<MemRefType>().getShape();
auto rank = operandShape.size();
auto loopCount = loopIVs.size();
std::vector<Value*> newLoopIVs;
std::vector<Value *> newLoopIVs;
for (unsigned reversedIdx = 0; reversedIdx < rank; ++reversedIdx) {
auto dimIdx = rank - 1 - reversedIdx;
auto loopIdx = loopCount - 1 - reversedIdx;
@ -218,8 +217,8 @@ getLoopIVsForBroadcasting(Location loc, ConversionPatternRewriter &rewriter,
// If its value is 1, it is broadcasted dimension.
// Otherwise, non-broadcasted dimension.
auto zero = rewriter.create<ConstantIndexOp>(loc, 0);
auto idx = rewriter.create<SelectOp>(loc, broadcastedDims[dimIdx],
zero, loopIVs[loopIdx]);
auto idx = rewriter.create<SelectOp>(loc, broadcastedDims[dimIdx], zero,
loopIVs[loopIdx]);
newLoopIVs.insert(newLoopIVs.begin(), idx);
} else {
// Non-broadcasted dimension
@ -260,26 +259,26 @@ struct ScalarOp<ONNXSubOp> {
template <>
struct ScalarOp<ONNXAndOp> {
using FOp = AndOp; // not use
using FOp = AndOp; // not use
using IOp = AndOp;
};
template <>
struct ScalarOp<ONNXOrOp> {
using FOp = OrOp; // not use
using FOp = OrOp; // not use
using IOp = OrOp;
};
template <>
struct ScalarOp<ONNXXorOp> {
using FOp = XOrOp; // not use
using FOp = XOrOp; // not use
using IOp = XOrOp;
};
template <>
struct ScalarOp<ONNXExpOp> {
using FOp = ExpOp;
using IOp = ExpOp; // not use
using IOp = ExpOp; // not use
};
template <>
@ -297,18 +296,19 @@ using ScalarIOp = typename ScalarOp<ElementwiseNaryOp>::IOp;
// Scalar unary ops for lowering to Krnl dialect.
//===----------------------------------------------------------------------===//
template <typename UnaryOp>
Value* mapToLowerScalarOp(Operation* op, ArrayRef<Type> result_types,
ArrayRef<Value*> operands, ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp(Operation *op, ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
/* Lower UnaryOp to Ops in the Standard dialect.
*/
auto loc = op->getLoc();
Type element_type = operands.front()->getType();
if (element_type.isa<IntegerType>()) {
return rewriter.create<ScalarIOp<UnaryOp>>(
loc, result_types, operands, mlir::None);
return rewriter.create<ScalarIOp<UnaryOp>>(loc, result_types, operands,
mlir::None);
} else if (element_type.isa<FloatType>()) {
return rewriter.create<ScalarFOp<UnaryOp>>(
loc, result_types, operands, mlir::None);
return rewriter.create<ScalarFOp<UnaryOp>>(loc, result_types, operands,
mlir::None);
} else {
emitError(loc, "unsupported element type");
return nullptr;
@ -319,13 +319,14 @@ Value* mapToLowerScalarOp(Operation* op, ArrayRef<Type> result_types,
// Scalar unary ops for lowering ONNXTanhOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXTanhOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXTanhOp>(Operation *op,
ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXTanhOp(%X) = DivFOp(SubFOp(ExpOp(%X), ExpOp(NegFOp(%X))),
// AddFOp(ExpOp(%X), ExpOp(NegFOp(%X))))
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
auto neg = rewriter.create<SubFOp>(loc, zero, operand);
@ -333,7 +334,7 @@ Value* mapToLowerScalarOp<ONNXTanhOp>(Operation* op,
auto negExp = rewriter.create<ExpOp>(loc, neg);
auto result =
rewriter.create<DivFOp>(loc, rewriter.create<SubFOp>(loc, exp, negExp),
rewriter.create<AddFOp>(loc, exp, negExp));
rewriter.create<AddFOp>(loc, exp, negExp));
return result;
}
@ -342,13 +343,14 @@ Value* mapToLowerScalarOp<ONNXTanhOp>(Operation* op,
// Scalar unary ops for lowering ONNXSinhOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXSinhOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXSinhOp>(Operation *op,
ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXSinhOp(%X) = DivFOp(SubFOp(ExpOp(%X), ExpOp(NegFOp(%X))),
// ConstantOp 2)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
auto two = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(2.0f));
@ -365,13 +367,14 @@ Value* mapToLowerScalarOp<ONNXSinhOp>(Operation* op,
// Scalar unary ops for lowering ONNXCoshOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXCoshOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXCoshOp>(Operation *op,
ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXCoshOp(%X) = DivFOp(AddFOp(ExpOp(%X), ExpOp(NegFOp(%X))),
// ConstantOp 2)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
auto two = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(2.0f));
@ -388,13 +391,14 @@ Value* mapToLowerScalarOp<ONNXCoshOp>(Operation* op,
// Scalar unary ops for lowering ONNXSigmoidOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXSigmoidOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXSigmoidOp>(Operation *op,
ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXSigmoidOp(%X) = DivFOp(ConstantOp 1,
// AddFOp(ConstantOp 1, ExpOp(NegFOp(%X))))
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
auto one = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(1.0f));
@ -410,9 +414,9 @@ Value* mapToLowerScalarOp<ONNXSigmoidOp>(Operation* op,
// Scalar unary ops for lowering ONNXHardSigmoidOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXHardSigmoidOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXHardSigmoidOp>(
Operation *op, ArrayRef<Type> result_types, ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// %Y = AddFOp(MulFOp(alpha, %X), beta)
// %Z = SelectOp(CmpFOp(OGT, %Y, Constant 0),
// %Y,
@ -421,7 +425,7 @@ Value* mapToLowerScalarOp<ONNXHardSigmoidOp>(Operation* op,
// %Z,
// Constant 1)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto alphaAttr = op->getAttrOfType<FloatAttr>("HardSigmoid.alpha");
auto betaAttr = op->getAttrOfType<FloatAttr>("HardSigmoid.beta");
@ -446,13 +450,14 @@ Value* mapToLowerScalarOp<ONNXHardSigmoidOp>(Operation* op,
// Scalar unary ops for lowering ONNXEluOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXEluOp>(Operation* op, ArrayRef<Type> result_types,
ArrayRef<Value*> operands, ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXEluOp>(Operation *op, ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXEluOp(%X) = SelectOp(CmpFOp(OLT, %X, ConstantOp 0),
// MulFOp(alpha, SubFOp(ExpOp(%X), 1)),
// %X)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto alphaAttr = op->getAttrOfType<FloatAttr>("Elu.alpha");
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
@ -461,9 +466,10 @@ Value* mapToLowerScalarOp<ONNXEluOp>(Operation* op, ArrayRef<Type> result_types,
auto exp = rewriter.create<ExpOp>(loc, operand);
auto lessThanZero =
rewriter.create<CmpFOp>(loc, CmpFPredicate::OLT, operand, zero);
auto result = rewriter.create<SelectOp>(loc, lessThanZero,
rewriter.create<MulFOp>(
loc, alpha, rewriter.create<SubFOp>(loc, exp, one)),
auto result = rewriter.create<SelectOp>(
loc, lessThanZero,
rewriter.create<MulFOp>(loc, alpha,
rewriter.create<SubFOp>(loc, exp, one)),
operand);
return result;
@ -473,14 +479,15 @@ Value* mapToLowerScalarOp<ONNXEluOp>(Operation* op, ArrayRef<Type> result_types,
// Scalar unary ops for lowering ONNXReluOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXReluOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXReluOp>(Operation *op,
ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXReluOp(%X) = SelectOp(CmpFOp(OLT, %X, ConstantOp 0),
// ConstantOp 0,
// %X)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
auto lessThanZero =
@ -494,14 +501,15 @@ Value* mapToLowerScalarOp<ONNXReluOp>(Operation* op,
// Scalar unary ops for lowering ONNXLeakyReluOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXLeakyReluOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *
mapToLowerScalarOp<ONNXLeakyReluOp>(Operation *op, ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXLeakyReluOp(%X) = SelectOp(CmpFOp(OLT, %X, ConstantOp 0),
// MulFOp(alpha, %X),
// %X)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto alphaAttr = op->getAttrOfType<FloatAttr>("LeakyRelu.alpha");
auto zero = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(0.0f));
@ -518,16 +526,17 @@ Value* mapToLowerScalarOp<ONNXLeakyReluOp>(Operation* op,
// Scalar unary ops for lowering ONNXSeluOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXSeluOp>(Operation* op,
ArrayRef<Type> result_types, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXSeluOp>(Operation *op,
ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXSeluOp(%X) = SelectOp(CmpFOp(OGT, %X, ConstantOp 0),
// MulFOp(gamma, %X),
// MulFOp(gamma,
// SubFOp(MulFOp(alpha, ExpOp(%X)),
// alpha)))
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto alphaAttr = op->getAttrOfType<FloatAttr>("Selu.alpha");
auto gammaAttr = op->getAttrOfType<FloatAttr>("Selu.gamma");
@ -537,9 +546,10 @@ Value* mapToLowerScalarOp<ONNXSeluOp>(Operation* op,
auto exp = rewriter.create<ExpOp>(loc, operand);
auto greaterThanZero =
rewriter.create<CmpFOp>(loc, CmpFPredicate::OGT, operand, zero);
auto select = rewriter.create<SelectOp>(loc, greaterThanZero, operand,
rewriter.create<SubFOp>(
loc, rewriter.create<MulFOp>(loc, alpha, exp), alpha));
auto select = rewriter.create<SelectOp>(
loc, greaterThanZero, operand,
rewriter.create<SubFOp>(loc, rewriter.create<MulFOp>(loc, alpha, exp),
alpha));
auto result = rewriter.create<MulFOp>(loc, gamma, select);
return result;
@ -549,11 +559,13 @@ Value* mapToLowerScalarOp<ONNXSeluOp>(Operation* op,
// Scalar unary ops for lowering ONNXReciprocalOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXReciprocalOp>(Operation* op, ArrayRef<Type> result_types,
ArrayRef<Value*> operands, ConversionPatternRewriter& rewriter) {
Value *
mapToLowerScalarOp<ONNXReciprocalOp>(Operation *op, ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXReciprocalOp(%X) = DivFOp(ConstantOp 1, %X)
auto loc = op->getLoc();
Value* operand = operands[0];
Value *operand = operands[0];
auto one = rewriter.create<ConstantOp>(loc, rewriter.getF32FloatAttr(1.0f));
auto result = rewriter.create<DivFOp>(loc, one, operand);
@ -565,14 +577,15 @@ Value* mapToLowerScalarOp<ONNXReciprocalOp>(Operation* op, ArrayRef<Type> result
// Scalar unary ops for lowering ONNXMaxOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXMaxOp>(Operation* op, ArrayRef<Type> result_types,
ArrayRef<Value*> operands, ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXMaxOp>(Operation *op, ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXMaxOp(%X, %Y) = SelectOp(CmpFOp(OGT, %X, %Y),
// %X,
// %Y)
auto loc = op->getLoc();
Value* lhs = operands[0];
Value* rhs = operands[1];
Value *lhs = operands[0];
Value *rhs = operands[1];
auto max = rewriter.create<CmpFOp>(loc, CmpFPredicate::OGT, lhs, rhs);
auto result = rewriter.create<SelectOp>(loc, max, lhs, rhs);
return result;
@ -582,14 +595,15 @@ Value* mapToLowerScalarOp<ONNXMaxOp>(Operation* op, ArrayRef<Type> result_types,
// Scalar unary ops for lowering ONNXMinOp
//===----------------------------------------------------------------------===//
template <>
Value* mapToLowerScalarOp<ONNXMinOp>(Operation* op, ArrayRef<Type> result_types,
ArrayRef<Value*> operands, ConversionPatternRewriter& rewriter) {
Value *mapToLowerScalarOp<ONNXMinOp>(Operation *op, ArrayRef<Type> result_types,
ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) {
// ONNXMinOp(%X, %Y) = SelectOp(CmpFOp(OLT, %X, %Y),
// %X,
// %Y)
auto loc = op->getLoc();
Value* lhs = operands[0];
Value* rhs = operands[1];
Value *lhs = operands[0];
Value *rhs = operands[1];
auto min = rewriter.create<CmpFOp>(loc, CmpFPredicate::OLT, lhs, rhs);
auto result = rewriter.create<SelectOp>(loc, min, lhs, rhs);
return result;
@ -599,10 +613,11 @@ Value* mapToLowerScalarOp<ONNXMinOp>(Operation* op, ArrayRef<Type> result_types,
//===----------------------------------------------------------------------===//
template <typename ElementwiseUnaryOp>
struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
ONNXElementwiseUnaryOpLowering(MLIRContext* ctx)
ONNXElementwiseUnaryOpLowering(MLIRContext *ctx)
: ConversionPattern(ElementwiseUnaryOp::getOperationName(), 1, ctx) {}
PatternMatchResult matchAndRewrite(Operation* op, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) const final {
PatternMatchResult
matchAndRewrite(Operation *op, ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) const final {
// TODO: Check that the types are valid.
// An element-wise unary operation must have all operands and the result of
// the same type. This should have been verified by the verifier.
@ -618,14 +633,14 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
// dimensions with the result at this pre-optimization phase.
// TODO: verify that dimensions match.
// TODO: can the dimension of the result differ after optimizations?
Value* alloc;
Value *alloc;
bool insertDealloc = checkInsertDealloc(op);
if (hasAllConstantDimensions(memRefType))
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
else
alloc = insertAllocAndDealloc(
memRefType, loc, rewriter, insertDealloc, {operands[0]});
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
{operands[0]});
// Number of loops
auto memRefShape = memRefType.getShape();
@ -633,7 +648,7 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
std::vector<Value*> originalLoops;
std::vector<Value *> originalLoops;
originalLoops.reserve(rank);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
@ -641,12 +656,12 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
// Define loop optimization.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
std::vector<Value*> optimizedLoops;
std::vector<Value *> optimizedLoops;
optimizedLoops.reserve(rank);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
Block& optimizationBlock = optimizedLoopsOp.region().front();
Block &optimizationBlock = optimizedLoopsOp.region().front();
KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
// Iterate over the loop nest.
@ -664,7 +679,7 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
}
auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
Block& iterationBlock = iterateOp.bodyRegion().front();
Block &iterationBlock = iterateOp.bodyRegion().front();
// Now perform the insertions into the body of the
// just generated instructions:
@ -681,7 +696,7 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
rewriter.setInsertionPointToStart(&iterationBlock);
// Handle the operation:
SmallVector<Value*, 4> loopIVs;
SmallVector<Value *, 4> loopIVs;
for (auto arg : iterationBlock.getArguments())
loopIVs.push_back(arg);
@ -701,10 +716,11 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
//===----------------------------------------------------------------------===//
template <typename ElementwiseVariadicOp>
struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
ONNXElementwiseVariadicOpLowering(MLIRContext* ctx)
ONNXElementwiseVariadicOpLowering(MLIRContext *ctx)
: ConversionPattern(ElementwiseVariadicOp::getOperationName(), 1, ctx) {}
PatternMatchResult matchAndRewrite(Operation* op, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) const final {
PatternMatchResult
matchAndRewrite(Operation *op, ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) const final {
// TODO: Check that the types are valid.
// An element-wise variadic operation must have all operands and the result
// of the same type. This should have been verified by the verifier.
@ -715,7 +731,7 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
// Insert an allocation and deallocation for the result of this operation.
auto memRefType = convertTensorToMemRef(tensorType);
Value* alloc;
Value *alloc;
bool insertDealloc = checkInsertDealloc(op);
// If the output has a dynamic dimension, we compute its dimension at
// runtime by using dimensions from the operands.
@ -725,8 +741,8 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
if (hasAllConstantDimensions(memRefType))
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
else
alloc = insertAllocAndDealloc(
memRefType, loc, rewriter, insertDealloc, operands);
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
operands);
// Number of loops
auto memRefShape = memRefType.getShape();
@ -734,7 +750,7 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
std::vector<Value*> originalLoops;
std::vector<Value *> originalLoops;
originalLoops.reserve(rank);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
@ -742,12 +758,12 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
// Define loop optimization.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
std::vector<Value*> optimizedLoops;
std::vector<Value *> optimizedLoops;
optimizedLoops.reserve(rank);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
Block& optimizationBlock = optimizedLoopsOp.region().front();
Block &optimizationBlock = optimizedLoopsOp.region().front();
KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
// Iterate over the loop nest.
@ -770,7 +786,7 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
getBroadcastedDimInfo(loc, rewriter, memRefType, operands);
auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
Block& iterationBlock = iterateOp.bodyRegion().front();
Block &iterationBlock = iterateOp.bodyRegion().front();
// Now perform the insertions into the body of the
// just generated instructions:
@ -786,7 +802,7 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
rewriter.setInsertionPointToStart(&iterationBlock);
// Handle the operation:
SmallVector<Value*, 4> loopIVs;
SmallVector<Value *, 4> loopIVs;
for (auto arg : iterationBlock.getArguments())
loopIVs.push_back(arg);
@ -812,35 +828,36 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
};
struct ONNXReshapeOpLowering : public ConversionPattern {
ONNXReshapeOpLowering(MLIRContext* ctx)
ONNXReshapeOpLowering(MLIRContext *ctx)
: ConversionPattern(mlir::ONNXReshapeOp::getOperationName(), 1, ctx) {}
PatternMatchResult matchAndRewrite(Operation* op, ArrayRef<Value*> operands,
ConversionPatternRewriter& rewriter) const final {
PatternMatchResult
matchAndRewrite(Operation *op, ArrayRef<Value *> operands,
ConversionPatternRewriter &rewriter) const final {
auto tensorType = (*op->result_type_begin()).cast<TensorType>();
auto loc = op->getLoc();
// Insert an allocation and deallocation for the result of this operation.
auto memRefType = convertTensorToMemRef(tensorType);
Value* alloc;
Value *alloc;
// Compute size in bytes.
Value* tensorSize = rewriter.create<ConstantOp>(loc,
rewriter.getIntegerAttr(
rewriter.getIntegerType(64), getMemRefEltSizeInBytes(memRefType)));
Value *tensorSize = rewriter.create<ConstantOp>(
loc, rewriter.getIntegerAttr(rewriter.getIntegerType(64),
getMemRefEltSizeInBytes(memRefType)));
bool insertDealloc = checkInsertDealloc(op);
if (hasAllConstantDimensions(memRefType)) {
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
} else {
auto memRefShape = memRefType.getShape();
SmallVector<Value*, 4> allocOperands;
SmallVector<Value *, 4> allocOperands;
for (int i = 0; i < memRefShape.size(); ++i) {
// The shape array can always be used to construct shape information of
// the result.
Value* index = rewriter.create<ConstantOp>(
Value *index = rewriter.create<ConstantOp>(
loc, rewriter.getIntegerAttr(rewriter.getIndexType(), i));
Value* loadedVal = rewriter.create<LoadOp>(loc, operands[1], index);
Value* int64LoadedVal = rewriter.create<ZeroExtendIOp>(
Value *loadedVal = rewriter.create<LoadOp>(loc, operands[1], index);
Value *int64LoadedVal = rewriter.create<ZeroExtendIOp>(
loc, loadedVal, rewriter.getIntegerType(64));
tensorSize = rewriter.create<MulIOp>(loc, tensorSize, int64LoadedVal);
allocOperands.push_back(rewriter.create<IndexCastOp>(
@ -851,7 +868,7 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
// Make sure to allocate at the beginning of the block if
// all dimensions are known.
auto* parentBlock = allocateMemref.getOperation()->getBlock();
auto *parentBlock = allocateMemref.getOperation()->getBlock();
if (insertDealloc) {
auto dealloc = rewriter.create<DeallocOp>(loc, allocateMemref);
dealloc.getOperation()->moveBefore(&parentBlock->back());
@ -874,7 +891,7 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
struct TensorTypeConverter : public TypeConverter {
using TypeConverter::TypeConverter;
LogicalResult convertType(Type t, SmallVectorImpl<Type>& results) override {
LogicalResult convertType(Type t, SmallVectorImpl<Type> &results) override {
if (auto tensor_type = t.dyn_cast<TensorType>()) {
results.push_back(convertTensorToMemRef(tensor_type));
return success();
@ -889,12 +906,12 @@ struct TensorTypeConverter : public TypeConverter {
/// inputs. Once unranked results can be handled gracefully this
/// override needs to be removed in favour of the original MLIR one.]
bool isSignatureLegal(FunctionType funcType) {
return llvm::all_of(
funcType.getInputs(), [this](Type type) { return isLegal(type); });
return llvm::all_of(funcType.getInputs(),
[this](Type type) { return isLegal(type); });
}
};
} // end anonymous namespace.
} // end anonymous namespace.
//===----------------------------------------------------------------------===//
// Frontend to Krnl Dialect lowering pass
@ -906,7 +923,7 @@ struct FrontendToKrnlLoweringPass
: public ModulePass<FrontendToKrnlLoweringPass> {
void runOnModule() final;
};
} // end anonymous namespace.
} // end anonymous namespace.
void FrontendToKrnlLoweringPass::runOnModule() {
auto module = getModule();
@ -943,32 +960,32 @@ void FrontendToKrnlLoweringPass::runOnModule() {
// Type conversion for function signatures.
// Call MLIR FuncOp signature conversion when result type is
// a ranked tensor.
populateFuncOpTypeConversionPattern(
patterns, &getContext(), tensor_to_memref_converter);
populateFuncOpTypeConversionPattern(patterns, &getContext(),
tensor_to_memref_converter);
// Frontent operation lowering.
patterns.insert<ONNXElementwiseUnaryOpLowering<mlir::ONNXExpOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXTanhOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXSinhOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXCoshOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXSigmoidOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXHardSigmoidOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXEluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXReluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXLeakyReluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXSeluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXReciprocalOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXAddOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXMulOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXDivOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXSubOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXAndOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXOrOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXXorOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXSumOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXMaxOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXMinOp>,
ONNXReshapeOpLowering>(&getContext());
ONNXElementwiseUnaryOpLowering<mlir::ONNXTanhOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXSinhOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXCoshOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXSigmoidOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXHardSigmoidOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXEluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXReluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXLeakyReluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXSeluOp>,
ONNXElementwiseUnaryOpLowering<mlir::ONNXReciprocalOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXAddOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXMulOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXDivOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXSubOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXAndOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXOrOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXXorOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXSumOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXMaxOp>,
ONNXElementwiseVariadicOpLowering<mlir::ONNXMinOp>,
ONNXReshapeOpLowering>(&getContext());
// With the target and rewrite patterns defined, we can now attempt the
// conversion. The conversion will signal failure if any of our `illegal`
@ -981,5 +998,5 @@ std::unique_ptr<Pass> mlir::createLowerToKrnlPass() {
return std::make_unique<FrontendToKrnlLoweringPass>();
}
static PassRegistration<FrontendToKrnlLoweringPass> pass(
"lower-frontend", "Lower frontend ops to Krnl dialect.");
static PassRegistration<FrontendToKrnlLoweringPass>
pass("lower-frontend", "Lower frontend ops to Krnl dialect.");

47
src/compiler/transform/lower_krnl.cpp
View File

@ -17,8 +17,8 @@ namespace {
struct KrnlIterateOpLowering : public OpRewritePattern<KrnlIterateOp> {
using OpRewritePattern<KrnlIterateOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(
KrnlIterateOp iterateOp, PatternRewriter& rewriter) const override {
PatternMatchResult matchAndRewrite(KrnlIterateOp iterateOp,
PatternRewriter &rewriter) const override {
auto boundMapAttrs =
iterateOp.getAttrOfType<ArrayAttr>(KrnlIterateOp::getBoundsAttrName())
.getValue();
@ -30,23 +30,23 @@ struct KrnlIterateOpLowering : public OpRewritePattern<KrnlIterateOp> {
operandItr++;
// Organize operands into lower/upper bounds in affine.for ready formats.
SmallVector<Value*, 4> lbOperands, ubOperands;
SmallVector<Value *, 4> lbOperands, ubOperands;
AffineMap lbMap, ubMap;
for (int boundType = 0; boundType < 2; boundType++) {
auto& operands = boundType == 0 ? lbOperands : ubOperands;
auto& map = boundType == 0 ? lbMap : ubMap;
auto &operands = boundType == 0 ? lbOperands : ubOperands;
auto &map = boundType == 0 ? lbMap : ubMap;
map = boundMapAttrs[boundIdx + boundType]
.cast<AffineMapAttr>()
.getValue();
operands.insert(
operands.end(), operandItr, operandItr + map.getNumInputs());
operands.insert(operands.end(), operandItr,
operandItr + map.getNumInputs());
std::advance(operandItr, map.getNumInputs());
}
nestedForOps.emplace_back(rewriter.create<AffineForOp>(
iterateOp.getLoc(), lbOperands, lbMap, ubOperands, ubMap));
rewriter.setInsertionPoint(nestedForOps.back().getBody(),
nestedForOps.back().getBody()->begin());
nestedForOps.back().getBody()->begin());
}
// Replace induction variable references from those introduced by a
@ -68,7 +68,7 @@ struct KrnlIterateOpLowering : public OpRewritePattern<KrnlIterateOp> {
auto innermostForOp = nestedForOps.back();
innermostForOp.region().getBlocks().clear();
rewriter.inlineRegionBefore(iterateOp.bodyRegion(), innermostForOp.region(),
innermostForOp.region().end());
innermostForOp.region().end());
rewriter.eraseOp(iterateOp);
return matchSuccess();
@ -80,11 +80,11 @@ struct KrnlIterateOpLowering : public OpRewritePattern<KrnlIterateOp> {
//===----------------------------------------------------------------------===//
class KrnlTerminatorLowering : public OpRewritePattern<KrnlTerminatorOp> {
public:
public:
using OpRewritePattern<KrnlTerminatorOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(
KrnlTerminatorOp op, PatternRewriter& rewriter) const override {
PatternMatchResult matchAndRewrite(KrnlTerminatorOp op,
PatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<AffineTerminatorOp>(op);
return matchSuccess();
}
@ -95,11 +95,11 @@ class KrnlTerminatorLowering : public OpRewritePattern<KrnlTerminatorOp> {
//===----------------------------------------------------------------------===//
class KrnlDefineLoopsLowering : public OpRewritePattern<KrnlDefineLoopsOp> {
public:
public:
using OpRewritePattern<KrnlDefineLoopsOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(
KrnlDefineLoopsOp op, PatternRewriter& rewriter) const override {
PatternMatchResult matchAndRewrite(KrnlDefineLoopsOp op,
PatternRewriter &rewriter) const override {
rewriter.eraseOp(op);
return matchSuccess();
}
@ -110,11 +110,11 @@ class KrnlDefineLoopsLowering : public OpRewritePattern<KrnlDefineLoopsOp> {
//===----------------------------------------------------------------------===//
class KrnlOptimizeLoopsLowering : public OpRewritePattern<KrnlOptimizeLoopsOp> {
public:
public:
using OpRewritePattern<KrnlOptimizeLoopsOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(
KrnlOptimizeLoopsOp op, PatternRewriter& rewriter) const override {
PatternMatchResult matchAndRewrite(KrnlOptimizeLoopsOp op,
PatternRewriter &rewriter) const override {
rewriter.eraseOp(op);
return matchSuccess();
}
@ -132,7 +132,7 @@ struct KrnlToAffineLoweringPass
: public FunctionPass<KrnlToAffineLoweringPass> {
void runOnFunction() final;
};
} // end anonymous namespace.
} // end anonymous namespace.
void KrnlToAffineLoweringPass::runOnFunction() {
auto function = getFunction();
@ -146,17 +146,18 @@ void KrnlToAffineLoweringPass::runOnFunction() {
OwningRewritePatternList patterns;
patterns.insert<KrnlIterateOpLowering, KrnlTerminatorLowering,
KrnlDefineLoopsLowering, KrnlOptimizeLoopsLowering>(&getContext());
KrnlDefineLoopsLowering, KrnlOptimizeLoopsLowering>(
&getContext());
if (failed(applyPartialConversion(getFunction(), target, patterns)))
signalPassFailure();
}
} // namespace
} // namespace
std::unique_ptr<Pass> mlir::createLowerKrnlPass() {
return std::make_unique<KrnlToAffineLoweringPass>();
}
static PassRegistration<KrnlToAffineLoweringPass> pass(
"lower-krnl", "Lower Krnl dialect.");
static PassRegistration<KrnlToAffineLoweringPass> pass("lower-krnl",
"Lower Krnl dialect.");

14
src/main.cpp
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@ -76,7 +76,7 @@ void LoadMLIR(string inputFilename, mlir::MLIRContext& context,
}
}
int main(int ac, char* av[]) {
int main(int ac, char *av[]) {
namespace po = boost::program_options;
po::options_description desc("ONNF available options");
@ -91,8 +91,8 @@ int main(int ac, char* av[]) {
po::positional_options_description p;
p.add("onnx-model", -1);
po::variables_map vm;
po::store(
po::command_line_parser(ac, av).options(desc).positional(p).run(), vm);
po::store(po::command_line_parser(ac, av).options(desc).positional(p).run(),
vm);
// TODO: allow multiple input files
assert(vm.count("onnx-model") < 2 && "At most one input file can be provided!");
@ -137,10 +137,10 @@ int main(int ac, char* av[]) {
// Write LLVM bitcode to disk.
std::error_code EC;
llvm::raw_fd_ostream moduleBitcodeStream(
"model.bc", EC, llvm::sys::fs::F_None);
llvm::WriteBitcodeToFile(
*mlir::translateModuleToLLVMIR(*module), moduleBitcodeStream);
llvm::raw_fd_ostream moduleBitcodeStream("model.bc", EC,
llvm::sys::fs::F_None);
llvm::WriteBitcodeToFile(*mlir::translateModuleToLLVMIR(*module),
moduleBitcodeStream);
moduleBitcodeStream.flush();
return 0;